Collect Information of Operational Cycle Times

Conducting a rapid improvement event in manufacturing is a straightforward process in which standard operational metrics are collected from the work operations that were identified using a VFM. These metrics were discussed in Chapter 3 and include value adding (VA), non-value adding (NVA), and business value adding (BVA) operations as well as production rates, scrap and rework percentages, equipment downtime percentages, available capacity, set-up times, inventory quantities, and the total floor area required by a process. In its data collection activities, a rapid improvement team moves into the process to collect information for the metrics for each operation on the VFM. This audit creates a snapshot of process performance. It also helps verify the original VFM quantification to show the team where it should focus its efforts for the remainder of the event.

In virtual IT processes that support services or manufacturing operations, when transactions are time stamped operation-by-operation, calculating waiting and processing times and overall lead times are easier. But if this information is not readily available, then business analytics assistance is required to obtain the data. It is important that analytics support be available for a rapid improvement event to enable quick access to electronic information both as pre-work, during, and after the event. Another difference in services is, rather than conducting a physical walkthrough, a virtual walkthrough by an algorithm traces the sequence of work task, i.e., the logic sequence associated with data manipulation as well as the calculations used in the transformation of the data.

Another difference unique to service operations is that it is difficult to measure work activities unless associates are in a pool. Transitions can be measured, but it is difficult to analyze how people do them. This information is important for understanding ways to improve a service process because work varies if dependent on human behavior. Data collection to measure service professional performance requires understanding how their activities are performed and the time spent performing each activity. This contrasts with manufacturing operations that have machines with a constant cycle time and employees trained to follow standard work instructions. The time elements are also well documented in manufacturing. These include work tasks and times for waiting, set-up, processing, unload inspection, and movement to the next operation. Because a rapid improvement event has a short time duration, e.g., days or weeks, it will be difficult to quantify cycle times and defect frequencies of highly manual work tasks without prior planning and data collection.

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